Multispectral Balanced Automatic Fault Diagnosis for Rolling Bearings under Variable Speed Conditions

Abstract

As a key component of machine, most rolling bearings operate under variable speed conditions. Therefore, it is critical to complete automatic fault diagnosis for rolling bearings under variable speed conditions. Although there have been many research studies on fault diagnosis in recent years, the following two problems still exist in fault diagnosis of variable speed bearing: (1) due to the large range of energy distribution for signals under variable speed conditions, the existed signal processing methods lead to the loss of fault information; (2) when directional filtering is carried out according to four different types of faults, the difference in amplitudes of the obtained spectrums is large. This means that the filtering result with the maximum amplitude will be determined as the fault type by mistake. In order to integrate the information scattered across different frequency spectrums and use reasonable filtering to complete automatic diagnosis, Multispectral Balanced Automatic Fault Diagnosis is proposed for rolling bearings under variable speed conditions. On the one hand, signals are preprocessed by the Multispectral Lossless Preprocessing Module, which can eliminate the influence of variable rotating speeds and avoid the loss of fault information. On the other hand, the Balanced Envelope Demodulation Module is designed to realize automatic fault diagnosis by Protrugram and Balancing Envelope Spectrum. The effectiveness of the proposed method is verified by simulated signals and experimental data. Results indicate that the method can complete automatic fault diagnosis of rolling bearings under variable speed conditions with an accuracy of 76%, which outperforms state-of-the-art methods.